Cisco Technology, Inc.

Appdynamics, Inc.

Ido Carmel

Raghvendra Singh

Takashi Arai

Andrew Morgan

Abhijit Sawant

Bhaskar Sunkara

Steffen Yount

Sastry Konduri

Sanjay Nagaraj

Jonathan Newcomb Swirsky Whitney

Apun Hiran

Jonathan Whitney

Abhi Sawant

In one aspect, a machine learning system for performing predictive analytics on database wait events is disclosed. The machine learning system includes a processor; a memory; and one or more modules stored in the memory and executable by a processor to perform operations including: receive database wait event data indicative wait events associated with database calls running on a monitored database; receive database performance data indicative of performance of the monitored database; correlate the received database wait event data with the received database performance data to obtain a correlation result; predict a performance issue with the monitored database based on the correlation result; and provide a user interface to display the predicted performance issue.

Predictive analytics on database wait events

Business transactions and the nodes processing the transactions may be monitored. Actions may be applied to one or more nodes when a performance issue is detected. A performance issue may relate to a metric associated with a transaction or node that processes the transaction. If a performance metric does not satisfy a health rule, the policy determines which action should be performed to correct the performance of the node. The corrective action may be applied to a node other than the node at which the performance metric is associated with. For example, if a performance metric for a first node does not satisfy a threshold, the corrective action may be applied to a second node. When a solution applied to a second node is found to correct the problem in first node, the solution may be applied to the other nodes experiencing the same problem.

Server performance correction using remote server actions

A system uses agents to monitor a distributed business transaction as well as monitor changes in software configuration. An agent may detect a file load, such a class load, obtain portions of the file such as functions, and perform a hash on the byte code functions. A hash tree may then be constructed and compared to previous states of the system. The hash tree may be generated, for example, at each file loading detected, so that system states can be compared. Differences in hash trees are detected and the changes are reported to an administrator of the system that provides the distrusted business transaction.

Automated software configuration management

Business transactions and the nodes processing the transactions are monitored and actions are applied to one or more nodes when a performance issue is detected. A performance issue may relate to a metric associated with a transaction or node that processes the transaction. If a performance metric determined from data captured by monitoring does not satisfy a health rule, the policy determines which action should be performed to correct the performance of the node. When a problem is detected for multiple nodes, the present technology may address a subset of the multiple nodes rather than apply an action to each node experiencing the problem. When a solution is found to correct the problem with the subset of nodes, the solution may be applied to the other nodes experiencing the same problem.

Group server performance correction via actions to server subset

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San Francisco, CA, United States of America

Ido Carmel